For pose-guided person image generation, proposed by [1]}, its goal is to convert the pose to the target pose while preserving the texture of the source image. This task is challenging due to the deformation between the source image and the target image, and the occlusion caused by the pose change. The current methods can be roughly divided into two categories: direct methods and warping-based methods. The direct method[1]}, [3]}, [4]}, [5]} uses the target pose, the source pose and the source image as the input of a vanilla convolutional network to directly generate an image. However, images generated in this way often lead to blurred results because the deformation between the source image and the target image is not considered. The warping-based method usually uses affine transformation[6]}, optical flow[7]}, [8]} or attention mechanism[9]} to establish the correspondence between the source image and the target image, and explicitly solves the deformation problem caused by the pose change. Compared with the direct method, it can generate More realistic images. But most of these methods only focus on pose-guided person image generation and cannot perform clothing-guided person image generation, due to the complex relationship between person attributes.
Different from the appeal method, a few approcaches combines the two tasks by extracting pose representation and texture features through independent encode modules. However, these approcaches cannot accurately render the texture details of the source image under the target view because they fail to model model the intricate interplay of the source image and target image.
